Criticality Metrics Study for Safety Evaluation of Merge Driving Scenarios, Using Near-Miss Video Data
Journal Article
09-12-01-0002
ISSN: 2327-5626, e-ISSN: 2327-5634
Sector:
Topic:
Citation:
Imaseki, T., Sugasawa, F., Kawakami, E., and Mouri, H., "Criticality Metrics Study for Safety Evaluation of Merge Driving Scenarios, Using Near-Miss Video Data," SAE Int. J. Trans. Safety 12(1):25-42, 2024, https://doi.org/10.4271/09-12-01-0002.
Language:
English
Abstract:
In autonomous driving vehicles with an automation level greater than three, the
autonomous system is responsible for safe driving, instead of the human driver.
Hence, the driving safety of autonomous driving vehicles must be ensured before
they are used on the road. Because it is not realistic to evaluate all test
conditions in real traffic, computer simulation methods can be used. Since
driving safety performance can be evaluated by simulating different driving
scenarios and calculating the criticality metrics that represent dangerous
collision risks, it is necessary to study and define the criticality metrics for
the type of driving scenarios. This study focused on the risk of collisions in
the confluence area because it was known that the accident rate in the
confluence area is much higher than on the main roadway. There have been several
experimental studies on safe driving behaviors in the confluence area; however,
there has been little study logically exploring the merging actions with
mathematical metrics. In light of this, this study introduces a criticality
metric representing the risk of a collision in a junction area. The metric
calculates the reaction level required to avoid a predicted collision risk;
therefore, a safety evaluation can be performed by assessing the reaction effort
to prevent such collisions in a driving scenario. The near-miss video data from
the database is used to validate the proposed metric for the merging scenario.
The database contains various real merging scenarios experienced by human
drivers. The proposed metric was validated to identify a critical situation with
collision risks and a safe driving situation that can prevent collisions easily,
using sample data of merging scenarios from the database. Moreover, an example
application for safety assessment was investigated. In summary, the safety
performance of autonomous driving vehicles in merging can be evaluated through
simulations using the criticality metric. In the future, the results of this
study could be applied to develop an on-board risk detection function in the
confluence area.